Sampling Scenarios Research Articles

Wind turbine blade icing diagnosis based on k-means clustering and label propagation algorithm

ABSTRACT Wind turbine blade icing seriously affects power generation performance and fatigue life, and effective diagnosis of blade icing is critical for mitigating icing effects. Current diagnostic methods are greatly affected by unbalanced data, especially for small sample data. To effectively solve the above problems, a novel diagnostic method combining k-means clustering with a label propagation algorithm is proposed. Specifically, k-means clustering handles unlabeled SCADA data, generating initial pseudo-labels. Then, the label propagation algorithm refines these pseudo-labels, enhancing labeling accuracy and overall classification performance. Finally, the effectiveness of the proposed method is validated using four different type classifiers for two wind farms. The results show that the proposed method improves the average diagnostic accuracy by 3.38% compared to models that eliminate unlabeled data and with a 4.2% improvement in small sample scenarios. The results demonstrate that the method exhibits high accuracy and significant generalization ability in diagnosing blade icing, offering practical benefits for data analysis and fault diagnosis.

Robust unsupervised 5D seismic data reconstruction on regular and irregular grids

Seismic data reconstruction has become a central focus in seismic data processing, addressing challenges posed by sparse sampling due to physical and budgetary constraints. The advent of 5D acquisition methodologies marks a significant advancement in the quality and completeness of seismic data sets. Most traditional 5D reconstruction methods commonly use the fast Fourier transform (FFT), requiring regular grids and preliminary 4D binning before 5D interpolation. Discrete Fourier transform and nonequidistant FFT can honor the original irregular coordinates. However, when using exact locations, these methods become computationally expensive. We introduce an unsupervised deep-learning methodology to learn a continuous function across the sampling points in seismic data, facilitating reconstruction on regular and irregular grids. The network comprises a multilayer perceptron with linear layers and element-wise periodic activation functions. It excels at mapping the input coordinates to the corresponding seismic data amplitudes without relying on external training sets. The network’s intrinsic low-frequency bias is crucial in prioritizing acquiring self-similar features over high-frequency and incoherent ones during training. This characteristic mitigates incoherent noise in seismic data, such as random and erratic components. To assess the robustness of the unsupervised reconstruction technique, we conduct comprehensive evaluations using synthetic data examples sampled regularly and irregularly, as well as field-data examples with and without binning. The findings demonstrate the efficacy of our deep-learning framework in achieving resilient and accurate seismic data reconstruction across diverse sampling scenarios.

Evaluation of model-integrated evidence approaches for pharmacokinetic bioequivalence studies using model averaging methods.

Conventional approaches for establishing bioequivalence (BE) between test and reference formulations using non-compartmental analysis (NCA) may demonstrate low power in pharmacokinetic (PK) studies with sparse sampling. In this case, model-integrated evidence (MIE) approaches for BE assessment have been shown to increase power, but may suffer from selection bias problems if models are built on the same data used for BE assessment. This work presents model averaging methods for BE evaluation and compares the power and type I error of these methods to conventional BE approaches for simulated studies of oral and ophthalmic formulations. Two model averaging methods were examined: bootstrap model selection and weight-based model averaging with parameter uncertainty from three different sources, either from a sandwich covariance matrix, a bootstrap, or from sampling importance resampling (SIR). The proposed approaches increased power compared with conventional NCA-based BE approaches, especially for the ophthalmic formulation scenarios, and were simultaneously able to adequately control type I error. In the rich sampling scenario considered for oral formulation, the weight-based model averaging method with SIR uncertainty provided controlled type I error, that was closest to the target of 5%. In sparse-sampling designs, especially the single sample ophthalmic scenarios, the type I error was best controlled by the bootstrap model selection method.

Implementing multiple imputations for addressing missing data in multireader multicase design studies

BackgroundIn computer-aided diagnosis (CAD) studies utilizing multireader multicase (MRMC) designs, missing data might occur when there are instances of misinterpretation or oversight by the reader or problems with measurement techniques. Improper handling of these missing data can lead to bias. However, little research has been conducted on addressing the missing data issue within the MRMC framework.MethodsWe introduced a novel approach that integrates multiple imputation with MRMC analysis (MI-MRMC). An elaborate simulation study was conducted to compare the efficacy of our proposed approach with that of the traditional complete case analysis strategy within the MRMC design. Furthermore, we applied these approaches to a real MRMC design CAD study on aneurysm detection via head and neck CT angiograms to further validate their practicality.ResultsCompared with traditional complete case analysis, the simulation study demonstrated the MI-MRMC approach provides an almost unbiased estimate of diagnostic capability, alongside satisfactory performance in terms of statistical power and the type I error rate within the MRMC framework, even in small sample scenarios. In the real CAD study, the proposed MI-MRMC method further demonstrated strong performance in terms of both point estimates and confidence intervals compared with traditional complete case analysis.ConclusionWithin MRMC design settings, the adoption of an MI-MRMC approach in the face of missing data can facilitate the attainment of unbiased and robust estimates of diagnostic capability.

Multi-combination fault data augmentation method of aero-engine gas path based on Extraction TimeGAN

Aero-engine is the core power component of aircraft, and the accurate processing of engine monitoring data is the key to ensure its reliable operation and prevent faults. In the use and maintenance of aero-engines, the multi-combination fault data acquisition of gas path system is expensive and unavailable, thus intelligent multi-combination fault diagnosis under small samples scenarios poses a significant challenge. Aero-engine gas path combination fault data exhibit strong coupling, making it difficult for data augmentation methods to produce high-quality data. To solve the above problem, a novel aero-engine gas path combination fault data augmentation method based on Extraction TimeGAN is proposed. First, original data of multi-combination faults were obtained from Gasturb. To increase the complexity of generating data, Gaussian white noise of Signal-to-Noise Ratio (SNR) 20 was injected into the original data. Then, new samples with similar characteristics were generated from the original data according to different fault categories by TimeGAN, and the original data and generated data were both mapped to the latent space by Principal Component Analysis (PCA). Finally, the optimized extraction mechanism of generated data was established, and the high-density original data was identified and divided in the latent space through Kernel Density Estimation (KDE), the high-density regional hypersphere was constructed by Support Vector Data Description (SVDD), and the generated data located inside the hypersphere is extracted as the optimized generated data. In the multi-combination fault diagnosis experiment of aero-engine gas path, Area Under the Curve (AUC) of Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) reaches 99.74%, 99.82% and 99.85%. Under high noise conditions, the AUC of Extraction TimeGAN only decreases 0.94%. Furthermore, Under the condition of small sample, Extraction TimeGAN maintains the AUC of over 99%.

Analysis of the Land Ecological Security Pattern in the Zhejiang Urban Circle under Small Sample Scenarios

Analysis of the Land Ecological Security Pattern in the Zhejiang Urban Circle under Small Sample Scenarios

DIGITAL SOLUTION FOR OPTIMIZATION OF FORMATION TESTING DESIGN

Evaluation of pumping time to receive a clean sample during fluid sampling job with wireline formation tester as well as evaluation of pumping scenarios to minimize sampling duration under existing constrains (reservoir and sandface constrains, safety etc.) are actual problems especially for fluid sampling in low-permeability formation. The article describes the digital solution allowing to simulate mud filtrate invasion in near-wellbore zone during well drilling and mud filtrate displacement by reservoir fluid during fluid sampling job with taking into account a formation damage dynamics. In this way, the software is able to evaluate depth of mud filtrate invasion and simulate both the multiphase inflow dynamics and the skin-factor dynamics during sampling job and predict sampling duration. To take into account a complex, essentially non-1D geometry of multiphase fluid flow in near-wellbore zone during fluid sampling, the special upscaling technique was developed. This upscaling technique is based on modification of 1D model by introduction of «effective» mud filtrate invasion profile and «geometry factor» which depend on ratio of vertical to horizontal permeabilities. The correlations between empirical model parameters and ratio of vertical to horizontal permeabilities were obtained by matching results of «pseudo 2D» simulations of pumping dynamics with results of 2D / 3D simulations conducted using commercial simulators ECLIPSE® and COMSOL Multiphysics®. The developed «pseudo 2D» model is computationally fast ones that allows to effectively conduct sensitivity study as well as adjust the model parameters and sampling scenario in real time in accordance with the real-time wireline formation tester measurements.

Voltage over-limit risk assessment of wind power and photovoltaic access distribution system based on day-night segmentation and Gaussian mixture model

The access of distributed power sources such as wind power and photovoltaic (PV) with randomness and uncertainty makes the operation of distribution system more complicated. It is particularly necessary to comprehensively and efficiently evaluate the risk of voltage over-limit in distribution systems with wind power and photovoltaic. Firstly, aiming at the problem of centralized sampling of Monte Carlo method in risk assessment of distribution system, a day-night segmented Monte Carlo (MC) sampling method is proposed to generate a large number of day-night sampling scenarios. Secondly, the Gaussian mixture model is used to fit the probability density of the voltage obtained from probabilistic power flow calculations, concurrently, the utility preference exponential function characterizes the severity of the over-limit, and a comprehensive evaluation model of voltage over-limit risk is constructed. Finally, the effectiveness and accuracy of the proposed method are verified by taking the improved IEEE 33-bus distribution system as an example. These results offer actionable insights for future risk assessments of new energy grid connections.

MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios

Bearing fault diagnosis is of great importance to ensure the safe and stable operation of mechanical equipment. The actual collected bearing fault signals are susceptible to strong noise interference and bearing samples for each fault state may be insufficient, which increases the difficulty of capturing effective features. Most of the existing diagnostic methods extract features from a single sensor signal for pattern recognition and fault diagnosis. The fault information provided by a single sensor is limited and incomplete, which is usually very difficult to meet the demand for accurate and reliable fault diagnosis in complex scenarios. To solve these problems, this paper proposes a multi-sensor residual convolutional fusion network (MRCFN) for intelligent fault diagnosis of bearings. Firstly, a convolutional pooling module (CPM) is coupled with the designed double ring residual module (DRRM) to rough feature extraction and deep feature mining, which not only captures the discriminative fault features from multi-sensor signal, but also avoids the performance degradation of network. Secondly, a spatial channel reconstruction module (SCRM) is further introduced to eliminate redundant information in the features and improve the network training efficiency. Finally, the presented global interactive perception fusion module (GIPFM) is connected with a classification block (CB) to globally fuse the features extracted from the acoustic and vibration signals and conduct automatic fault identification, which both can realize the complementarity and calibration of multi-sensor feature information and high precision diagnosis. The experiments and a series of comparisons are implemented on two datasets to efficaciously verify the superiority of the proposed method over five existing representative multi-sensor fusion diagnosis methods (i.e., FAC-CNN, MB-CNN, MsACNN, MRSDF, and IMSFDFL) under strong noise and small samples.

Tunable Nanoislands Decorated Tapered Optical Fibers Reveal Concurrent Contributions in Through-Fiber SERS Detection.

Creating plasmonic nanoparticles on a tapered optical fiber (TF) tip enables a remote surface-enhanced Raman scattering (SERS) sensing probe, ideal for challenging sampling scenarios like biological tissues, site-specific cells, on-site environmental monitoring, and deep brain structures. However, nanoparticle patterns fabricated from current bottom-up methods are mostly random, making geometry control difficult. Uneven statistical distribution, clustering, and multilayer deposition introduce uncertainty in correlating device performance with morphology. Ultimately, this limits the design of the best-performance remote SERS sensing probe. Here we employ a tunable solid-state dewetting method to create densely packed monolayer Au nanoislands with varied geometric parameters in direct contact with the silica TF surface. These patterns exhibit analyzable nanoparticle sizes, densities, and uniform distribution across the entire taper surface, enabling a systematic investigation of particle size, density, and analyte effects on the SERS performance of the through-fiber detection system. The study is focused on the SERS response of a widely employed benchmark molecule, rhodamine 6G (R6G), and serotonin, a highly relevant neurotransmitter for the neuroscience field. The numerical simulations and limit of detection (LOD) experiments on R6G show that the increase of the total near-field enhancement volume promotes the SERS sensitivity of the probe. However, we observed a different behavior for serotonin linked to its interaction with the nanoparticle's surface. The obtained LOD is as low as 10-7 M, a value not achieved so far in a through-fiber detection scheme. Therefore, our work offers a strategy to design nanoparticle-based remote SERS sensing probes and provides new clues to discover and understand intricate plasmonic-driven chemical reactions.

Minimax rates of convergence for sliced inverse regression with differential privacy

Sliced inverse regression (SIR) is a highly efficient paradigm used for the purpose of dimension reduction by replacing high-dimensional covariates with a limited number of linear combinations. This paper focuses on the implementation of the classical SIR approach integrated with a Gaussian differential privacy mechanism to estimate the central space while preserving privacy. We illustrate the tradeoff between statistical accuracy and privacy in sufficient dimension reduction problems under both the classical low- dimensional and modern high-dimensional settings. Additionally, we achieve the minimax rate of the proposed estimator with Gaussian differential privacy constraint and illustrate that this rate is also optimal for multiple index models with bounded dimension of the central space. Extensive numerical studies on synthetic data sets are conducted to assess the effectiveness of the proposed technique in finite sample scenarios, and a real data analysis is presented to showcase its practical application.

A method for predicting methane production from anaerobic digestion of kitchen waste under small sample conditions.

Anaerobic digestion (AD) has the great potential to treat organic waste and achieve remarkable results effectively. However, it is very tough to establish an accurate mechanistic model for this process. Data-driven modeling technology has opened a new door to solving this problem. While when the sample set is small, traditional data-driven modeling methods are often powerless. In this paper, an effective method is proposed for data-driven high-precision modeling in small sample scenarios. A time series generative adversarial network (TimeGAN) is first utilized to augment the original high-quality small-sample data collected during the AD methane production. A novel hybrid kernel extreme learning machine (HKELM) is then designed to form a better structure of the data-driven model, whose regularization coefficient C0 is optimized by the sparrow search algorithm (SSA). Finally, this semi-finished model (SSA-HKELM) is trained by the augmented data to form the final mathematical model (TimeGAN-SSA-HKELM) for the AD methane generation process. Comparative experiments of the methane daily production prediction error haveverified the effectiveness of the method, which can be extended to other similar small sample data-driven modeling scenarios.

Domain-consistent syntactic representation for cross-domain aspect sentiment triplet extraction

Aspect Sentiment Triplet Extraction (ASTE) aims to identify triplets consisting of aspect terms, opinion terms, and sentiment polarities in reviews. Although previous methods of training specific models for a single domain have achieved refreshing results, the assumption that there are enough labeled samples in each domain does not always hold, and the performance of most models in domains lacking labeled samples can suffer significant degradation. To this end, we explore the ASTE task under a cross-domain setting to solve the problem of sentiment triplet extraction in unlabeled sample scenarios. In this paper, we propose a domain-consistent syntactic representation method, which aims to transfer the knowledge mined from resource-rich source domains to unlabeled target domains by minimizing the discrepancy of syntactic representations between domains. Specifically, we design a part-of-speech perception network that captures the part-of-speech rules of words playing the role of task-related entities and a dependency perception network that learns potential dependencies between words. Meanwhile, Maximum Mean Discrepancy (MMD) is adopted to minimize the discrepancy of syntactic representations containing rule information between domains to obtain domain-consistent syntactic representation, thereby transferring mined knowledge and providing domain-invariant features for classifier decision-making. Extensive experimental results on eight different domain datasets demonstrate the superiority of our method.

Sign‐flip inference for spatial regression with differential regularisation

SummaryWe address the problem of performing inference on the linear and nonlinear terms of a semiparametric spatial regression model with differential regularisation. For the linear term, we propose a new resampling procedure, based on (partial) sign‐flipping of an appropriate transformation of the residuals of the model. The proposed resampling scheme can mitigate the bias effect induced by the differential regularisation. We prove that the proposed test is asymptotically exact. Moreover, we show, by simulation studies, that it enjoys very good control of Type‐I error also in small sample scenarios, differently from parametric alternatives. Additionally, we show that the proposed test has higher power with respect than recently proposed nonparametric tests on the linear term of semiparametric regression models with differential regularisation. Concerning the nonlinear term, we develop three different inference approaches: a parametric one and two nonparametric alternatives. The nonparametric tests are based on a sign‐flip approach. One of these is proved to be asymptotically exact, while the other is proved to be exact also for finite samples. Simulation studies highlight the good control of Type‐I error of the nonparametric approaches with respect the parametric test, while retaining high power.

Impact of Varying Number of Radio Occultation Observations on Regional Weather Prediction Over India During the Summer Monsoon Season

AbstractObserving system simulation experiments are carried out to investigate the added value of radio occultation (RO) refractivity observations with various spatial sampling scenarios on regional weather predictions across the Indian region. A full summer monsoon season (June through September 2020) was used to demonstrate how varying the numbers and horizontal resolutions of RO data impacted regional‐scale weather forecasting. The MPAS (Model for Prediction Across Scales) was used to produce a nature run at a maximum horizontal resolution of 10 km. Then the WRF model with 12‐km horizontal resolution was used to carry out assimilation/forecast experiments with varying number of simulated RO observations. When the performance of the experiments is taken into account for moisture, temperature, winds and rainfall, as well as prediction lengths, the results show that RO observations with 50‐km resolution assimilated every 6 hr would provide the best results. Increasing the horizontal resolution to 25 km per 6 hr shows little overall improvement. Furthermore, RO data with horizontal resolutions lower than 100 km per 6 hr have only a small impact on the regional numerical weather prediction system. The number of low Earth orbit satellites in low‐inclination orbits required to achieve occultations every 6 hr with 50‐km resolution based on the COSMIC‐2 mission is approximately 700. This work is relevant for the deployment of the cost‐effective RO observing system for improved weather forecasting over the Indian region.

Intraday optimal scheduling method for microgrid in small sample scenarios

Intraday optimal scheduling method for microgrid in small sample scenarios

A method for updating variable radius plot surveys

Variable radius plot surveys can require updated estimates when the quality of previous estimates have declined over time. We developed a statistical estimator for this purpose that is the product of two estimators, one estimator is for the updated volume-to-basal-area ratio and the other estimator is for the updated basal area. Our estimator relies only on the surveyor identifying previously measured trees and does not require the identification of ingrowth, which simplifies its application. We also propose a variance estimator that relies on the bootstrap method. We investigate the point and variance estimators under repeated sampling of stem-mapped populations that include five measurements over a period of approximately 20 years. Based on 144 sampling scenarios, our estimator demonstrated relative biases ranging between -0.3% and 0.8%. The bootstrap variance estimator tended to be deflated, with relative absolute biases ranging between -8% to 4%. When revisiting one third of the initially installed plots, our estimator obtained relative root mean squared errors within 1.6% of an independent resurvey effort that does not use the prior survey. This implies that our estimator can substantially reduce field work while producing surveys of approximately the same quality when updating prior surveys.

Targeted stereology: sampling heterogeneously distributed structures and lesions in the lung

Stereology, the gold standard of lung morphometry, critically depends on sampling of tissue for analysis. Random sampling approaches guarantee each part of the organ an equal chance of being included in the analysis; hence, they guarantee a representative sample of the whole. However, when biological or pathological structures of interest are rare and/or heterogeneously distributed over the whole lung, the random sampling approach can be inefficient or even result in meaningless data. In such cases, a targeted sampling approach can be useful, which helps to relate the analytical items to an appropriate reference space. Targeted stereology greatly benefits from the increasing availability of multiresolution imaging techniques at macroscopic and microscopic levels as well as digital tools of segmentation. As such, this article outlines two basic sampling scenarios: 1) In the first scenario, computed tomography and microscopy are subsequently used to segment the airway/arterial tree and perform stereological measurements on specific branches of the tree. 2) The second scenario deals with the heterogeneous distribution of pathological lesions. This type of analysis can be divided into two stages: assessment of lesions of interest (LOIs) within the lung and assessment of subcompartments within LOIs. Taken together, targeted stereology has a thorough foundation in stereological theory and is able to not only significantly increase the efficiency of the analysis but also yield new types of information that would be lost with the classical random sampling approach.

A study on factors influencing the spatial oppressiveness in small courtyard spaces within university residential areas

ABSTRACT A number of obstacles in terms of the growing demand for residential spaces for students in China's education industry. Changing urban development patterns often lead to densely populated, unpleasant university residential neighborhood, posing threats to the well-being of university personnel. This study addresses the limitations of past research, focusing on a real campus's enclosed high-rise student apartment courtyard. Using three key design variables (courtyard area, building height and ground-level interface transparency), 27 sample scenarios were developed for psychological experiments. The findings indicate that while visual scene complexity has minimal impact on oppression to confined environments, building exposure and sky visibility do. People's perception of openness is determined by building exposure, sky visibility, and visual complexity. Higher visual complexity and greater sky visibility enhance the sense of openness. Satisfaction is primarily influenced by sky visibility. The study also conducts a multiple linear regression of spatial and psychological indicators and discusses the tolerance threshold for spatial oppressiveness and the spatial oppressiveness under various spatial types of samples.

Testing the fit of data and external sets via an imprecise Sargan-Hansen test

In empirical sciences such as psychology, the term cumulative science mostly refers to the integration of theories, while external (prior) information may also be used in statistical inference. This external information can be in the form of statistical moments and is subject to various types of uncertainty, e.g., because it is estimated, or because of qualitative uncertainty due to differences in study design or sampling. Before using it in statistical inference, it is therefore important to test whether the external information fits a new data set, taking into account its uncertainties. As a frequentist approach, the Sargan-Hansen test from the generalized method of moments framework is used in this paper. It tests, given a statistical model, whether data and point-wise external information are in conflict. A separability result is given that simplifies the Sargan-Hansen test statistic in most cases. The Sargan-Hansen test is then extended to the imprecise scenario with (estimated) external sets using stochastically ordered credal sets. Furthermore, an exact small sample version is derived for normally distributed variables. As a Bayesian approach, two prior-data conflict criteria are discussed as a test for the fit of external information to the data. Two simulation studies are performed to test and compare the power and type I error of the methods discussed. Different small sample scenarios are implemented, varying the moments used, the level of significance, and other aspects. The results show that both the Sargan-Hansen test and the Bayesian criteria control type I errors while having sufficient or even good power. To facilitate the use of the methods by applied scientists, easy-to-use R functions are provided in the R script in the supplementary materials.